Geomorphology 241 (2015) 175–191

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Geomorphology

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Longshore variability of beach states and bar types in a microtidal,storm-influenced, low-energy environment

N. Aleman a,⁎, N. Robin a, R. Certain a, E.J. Anthony b, J.-P. Barusseau a

a Perpignan University, Centre Européen de Formation et de Recherche sur les Environnements Méditerranéens (CEFREM), UMR5110, 52 Avenue Paul Alduy, 66860 Perpignan Cedex 9, Franceb Aix-Marseille University & Institut Universitaire de France, Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement (CEREGE), UM 34, Europôle Méditerranéen del'Arbois, BP 80, 13545 Aix-en-Provence Cedex 4, France

⁎ Corresponding author. Tel.: +33 468662057.E-mail address: nicolas.aleman@univ-perp.fr (N. Alem

http://dx.doi.org/10.1016/j.geomorph.2015.03.0290169-555X/© 2015 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 18 September 2014Received in revised form 10 March 2015Accepted 15 March 2015Available online 17 April 2015

Keywords:Beach statesBar typesGeological contextLiDARNearshore barsDean parameter

Beach classificationmodels are widely used in the literature to describe beach states in response to environmen-tal conditions. These models were essentially developed for sandy barred to barless beaches in micro- to meso-tidal environments subject to moderate to high wave energy conditions and have been based on field studiesover limited stretches of coast. Here,we further interrogate the performance of the Australian beach classificationscheme by analysing beach states and corresponding bar types on a regional scale in a storm-influenced, lowwave-energy, microtidal environment, using a large and unique spatial and temporal dataset of supra- andsubtidal beach morphology and sedimentology. The 200 km-long coast of the Gulf of Lions in theMediterraneanconsists of quasi-continuous sandy beaches with a well-developed double sandbar system. All the reported clas-sical beach states were observed on this coast, from reflective to dissipative, along with twomore unusual states:the rock platform-constrained beach state which is associated with bedrock outcrops, and the non-barred dissi-pative beach statewhich ismore commonly found in large tidal-range settings. LiDAR bathymetry shows that thetransitions between beach state zones are marked mainly headlands but transitions also occur progressivelyalong stretches of continuous sandy beach. The longshore distribution of beach states and associated bar typeson a regional scale can be related to the variability of hydrodynamic conditions (wave incidence and energy)and sediment characteristics (particle size). However, the influence of these parameters on beach state seemsto be largely controlled by the geological context such as the presence of a river mouth, headland or rock plat-form. Finally, we assessed the ability of the parameter Ω, commonly used to characterise beach states, whichcombines wave characteristics and sediment fall velocity, to predict the observed beach states and bar typesusing a very large set of hydrodynamic and sedimentary data. Our results, based on high frequency spatial sam-pling, show that the fall velocity of the subtidal sediment coupled with wave statistics one month prior the ob-served beach state strongly improved the predictive power of the parameter Ω.

© 2015 Elsevier B.V. All rights reserved.

1. Introduction

Beaches play a major environmental role as buffers of wave energyand storms (Wright and Thom, 1977). One major focus of coastal re-search is to relate environmental parameters to beach morphologicalchange (Cowell and Thom, 1994). Many beach studies assume that asystem can move towards a state of dynamic equilibrium under steadyforcing conditions (wave climate, tidal regime and beach sedimentcharacteristics), following the morphodynamic postulate of Wrightand Thom (1977). Consequently, various conceptual beach modelsbacked, or not, by simple semi-quantitative parameters have been pro-posed, enabling a better understanding of beachmorphological systems(Sunamura and Takeda, 1984; Wright and Short, 1984; Lippmann andHolman, 1990; Short, 1991, 1992; Masselink and Short, 1993; Short

an).

and Aagaard, 1993; Levoy et al., 1998; Scott et al., 2011). These modelsalso throw light on other aspects of beach dynamics, such as variationsin beach volume, basic hydrodynamic signatures and sediment move-ment. Wright and Short (1984) developed a now commonly used em-pirical model that classified beach states according to the parameter Ω(Dean, 1973), also commonly known as the dimensionless fall velocity,given by:Ω= Hb/WsTwhere Hb represents wave breaker height,Ws thesediment fall velocity, and T the wave period. Wright and Short (1984)suggested thatΩmust be less or equal to 1 for a beach to be fully reflec-tive, and greater or equal to 6 in order for it to be in a fully dissipativestate. Intermediate beach states tend to occur for Ω between 1 and 6,and are characterised by variable nearshore bar systems: Low Tide Ter-race (LTT), Rhythmic Bar and Beach (RBB), Transverse Bar and Rip (TBR)and Longshore Bar-Trough (LBT). Later, Masselink and Short (1993)proposed a beach state model for meso- and macro-tidal beachesusing the Relative Tidal Range (RTR) and established correspondingcharacteristic values of Ω. Other dimensionless indices (surf similarity

176 N. Aleman et al. / Geomorphology 241 (2015) 175–191

parameter (Battjes, 1974) and surf scaling parameter (Guza and Inman,1975)) based onwave and beach slope characteristics are less used. TheWright and Short (1984) model has been widely used to elaborate re-gional or local beach state classification schemes (e.g., Short, 1992,2006; Calliari et al., 1996; Benedet et al., 2004b; Costas et al., 2005;Castelle et al., 2007; Sénéchal et al., 2009; Price and Ruessink, 2011).Most beach state studies have been conducted on sandy beaches domi-nated by moderate to high wave conditions, and only limited researchhas been undertaken on beaches subject to low wave energy, whichhave been shown to respond very differently to changing wave condi-tions (Hegge et al., 1996; Jackson et al., 2002; Costas et al., 2005;Gómez-Pujol et al., 2007).

Whatever the wave-energy context, several studies have found lowcorrelations between the predicted and observed beach morphologies(Wright et al., 1987; Sanderson and Eliot, 1999; Jackson et al., 2005;Gómez-Pujol et al., 2007; Grasso et al., 2009; Loureiro et al., 2013).This poor relationship has been justified by theweakness of the discrim-inatory capacity of the parameter Ω under certain conditions. Indeed,the original sediment and wave parametric characterisations of thebeach state model of Wright and Short (1984) were essentially drawnfrom swell-dominated beaches in Australia enclosed between bedrockheadlands and commonly dominated by cross-shore sand exchangesbetween the beach and the nearshore zone. This can be a source of dis-crepancy whenΩ is deduced in other types of wave environments. Thepredictive power of sediment-wave parameters to characterise beacheshas been questioned (Anthony, 1998). In some circumstances, incidentwave heights and periods may vary temporally and alongshore, proba-bly as a result of harmonic decoupling and wave reforming over a com-plex changing nearshore morphology, coastline orientation or seasonaleffects. Median grain size and mean settling velocity may also varymarkedly over time andno standardized rules on sediment sampling lo-cations are clearly recommended. Such considerations of procedurehave received little attention but do affect the representativeness ofsediment-wave parametric combinations in relation to the beach envi-ronment (Anthony, 1998; Scott et al., 2011; Short and Jackson, 2013).Recently, geological setting has been highlighted as another controllingfactor of beach states, notably through headlands, rock platforms, sedi-ment supply and accommodation space (e.g., Cooper and Pilkey, 2004;Jackson et al., 2005; Vousdoukas et al., 2005; Short, 2006, 2010;Backstrom et al., 2009; Jackson and Cooper, 2009; Muñoz-Perez andMedina, 2010; Loureiro et al., 2013). Notwithstanding these various res-ervations, Ω has been successfully used recently in equilibrium modelspredicting shoreline evolution (Davidson et al., 2013; Splinter et al.,2014).

Consequently, the discrimination and influence of each control pa-rameter are difficult to determine and many more case studies fromcontrasting environments are needed for a better morphodynamiccharacterisation of beach states. In particular, a key element in mappingand understanding the spatial representation of bar-beach states andtheir longshore variability is that of having accurate 3D data and on alarge spatial scale, and over the long term, in order to bring out equilib-rium beach states. Previous observations of beach states are commonlybased on aerial photographs, which have the advantage of good spatialextension (regional scale) but limited 3D restitution, as well as sporadictopo-bathymetric data (DGPS, video) of low spatial extension. Theselimitations have probably encouraged the use of the parameter Ω inspite of the limitations highlighted above. Furthermore, many of the re-ported case studies in the literature are beach sites with well-markedboundaries (headlands, rocks, reef, islets or islands) inducing a short av-erage beach length and individual beach state compartments that canbe more readily characterised (Jackson et al., 2005; Short, 2006;Gómez-Pujol et al., 2007; Klein et al., 2010; Scott et al., 2011; Loureiroet al., 2013). The spatial transition between bar-beach states on longopen beaches without such boundaries has been poorly documented.

This paper complements recentwork on the typologies of nearshorebars in the study area (Aleman et al., 2011), and analyses spatial beach

state variability along 200 km of microtidal low-energy sandy coast inthe western Gulf of Lions, France (Fig. 1). The main approach adoptedhere lies in the use of very high-resolution 3D LiDAR topo-bathymetricdata, which have the advantage of representing beach states and espe-cially their transitions with good accuracy on a regional scale comparedto traditional technologies (bathymetric and video survey). In additionto the LiDAR data, the results of previous studies and input from aerialphotographs have also been used to obtain a robust determination ofmodal beach states. The objectives are to: (1) determine the range ofmodal beach states associated with a suite of more or less complexbar morphologies with respect to classifications proposed in the litera-ture, and illustrate and describe the longshore transitions between thedifferent beach states; (2) identify the role of geological constraints ininfluencing beach states and their transitions, as well as the influenceof engineering structures on such states and transitions; and (3) assessthe influence of cross-shore distribution of sediment size and wave cli-mate variability on the performance of Ω in a microtidal low-energysandy coast.

2. Study area

The study area, located along the Languedoc-Roussillon coast of theGulf of Lions is a Holocene curvilinear lowland unit stretching over200 km and comprising narrow sand barriers that isolate several la-goons (Fig. 1). Since the 1970s, large tourist complexes have beenbuilt on many of these barriers which are bound by rocky headlands,river mouths and several harbours. The upper shoreface displays a suc-cession of 1 to 3 bars and troughs that vary in shape (linear or crescen-tic) and depth from one site to another (Barusseau and Saint-Guily,1981; Aleman et al., 2011) (Fig. 1A).

Aleman et al. (2011) defined the various bar types (Dissipative (D),Longshore Bar-Trough (LBT), Transverse Bar and Rip (TBR), RhythmicBar and Beach (RBB), Low Tide Terrace (LTT) and Reflective (R)) onthe basis of the model proposed by Wright and Short (1984), andmapped their distributions, pointing out different intermediate andcomplex bar types. In the southern part (40.5 km of coastline), theinner bar (IB) and the outer bar (OB) are crescentic. The inner bar alter-nates between the TBR and RBB types, with many transitional TBR/RBBshapes. In the central part (78.3 km), the inner bar is mainly crescenticRBB or TBR with 47% of oblique configurations. The outer bar displaysmainly a straight configuration with LBT or Dmorphologies. The north-ern part (69.4 km) is different from the other two. To the west, the sys-tem exhibits a double straight bar configuration (LBT or D). The barsystem then displays one or no bar to the east.

The nearshore slope pattern along the Gulf of Lions is depicted inFig. 2. The upper shoreface (including the nearshore bar system)shows significant longshore variation at local and regional scales (be-tween 0.37° and 2.47°) due to the presence of large bars and the geolog-ical setting (river-mouth pro-deltas or bedrock outcrops) as well asanthropogenic structures (harbours or coastal defences). The lowershoreface (from the outer edge of the bar system to 1 km offshore)and the entire shoreface (from the shoreline to the offshore limits ofLiDAR detection at about −20 m depth) are very similar between0.08° and 1.35°. Gentle slopes (0.1° to 0.2°) correspond to zoneswhere bedrock outcrops occur (southern part of the coast near the Pyr-enees, Cape Leucate, and the subtidal rock platforms of Aresquiers andPalavas-Maguelone). Overall, the three parts of coast exhibit the samelongshore northward decreasing, then increasing, trend in slope(Fig. 2). In the southern part, the gradient of the entire shoreface variesbetween 0.74° and 1.32°, whereas in the central and northern part it os-cillates around 0.46°. A net decrease in slope occurs at Cape Leucate(Fig. 2). Changes in other environmental parameters such as bar pat-terns or sediment characteristics are also observed in the vicinity ofthe cape. Generally, a slope increase occurs on the right side of coastalrivermouths (except for theOrb andHérault in the central part), updriftof Capes Leucate andAgde, and at the distal end of Espiguette spit on the

Fig. 1. Map of the Gulf of Lions showing: (A) the general pattern of bars and longshore drift, and (B) wave conditions and directions.Adapted from Certain, 2002.

177N. Aleman et al. / Geomorphology 241 (2015) 175–191

Rhône delta (Fig. 2). Conversely, the slope decreases downdrift of bothcapes. The mean depth of wave closure on this coast is around −6and −8 m (Durand, 1999; Sabatier et al., 2004). In the northern area,the substratum outcrops at depths of −5 to −10 m forming a widerock platform. Consequently, the sedimentary stock is limited andclose to the shore. Geophysical data show that sand thickness does notexceed 4 m, and sometimes bedrock crops out on the inner and outertroughs of bars (Certain et al., 2005b). An estimation of the sedimentbudget for this coast over the period 1984–2009 shows that erosionhas been dominant, with a volume loss of −30.2 ± 4.2 × 106 m3

(Brunel et al., 2014).The Gulf of Lions is a microtidal, wave-dominated environment. The

tidal range is very low (b0.30 m at mean spring tides). Nevertheless,large variations in water level can occur in response to wind forcingand atmospheric pressure fluctuations. Set-up can attain 1 m near theshore (Certain, 2002) under the combined action of storm surge andwaves. Two wind orientations prevail: NW offshore winds (60% of thetime) and E to SE onshore winds (30% of the time) (Casanobe, 1961;Person, 1973; Cattaliotti-Valdina, 1978; Mayençon, 1992). Significant

Fig. 2. Longshore evolution of the slope for the upper shoreface,

wave heights (Hs) impinging on the coast are generally low(Hs b 0.3m for 75%of the time andHs b 1.5m for 94%),withpeakperiods(Tp) between 5.7 and 6.3 s. Wave heights larger than 3 m are observedless than 1% of the time, with Tp between 5 and 10 s. Wave heightscan reach 7 m during exceptional storms (1982, 1997 and 2003) withperiods around 12 s. The maximum wave heights (Hmax) at Sète are 5,7.8 and 9 m for return periods of 1, 10 and 100 years, respectively(L.C.H.F., 1984). Waves from SW to N are generated by offshore windsand are low, whereas waves from E to S are generated by onshorewinds and are the most energetic (Fig. 1B). Table 1 shows the temporalvariation of wave characteristics assessed from the monthly and annualwave data for four stations along the Gulf of Lions. Spring and summer(April–September) are characterised by low waves (0.42–0.60 mmonthly mean height) with few major storms (3.67 m max height atLeucate and Sète). The standard deviation is low, between 0.25 and0.43 (Table 1). July, August and September are themonthswith the low-est waves (0.42–0.50mmonthlymean height). The autumn andwinterperiod (October–March) displays the highest waves, although themonthly mean height is not much higher (0.76–0.90 m). During this

lower shoreface and entire shoreface (up to 1 km offshore).

Table 1Annual and seasonal statistics of significantwave height (Hs) in the Gulf of Lions for the fourwave buoys (4–10 year record period).N: number of records;Min: minimumsignificantwaveheight;Max: maximum significantwave height;X: mean significantwave height; σ: standard deviation of significantwave height; 0–1.5m: percentage of wave height between 0 and 1.5m; 1.5–3 m: percentage of wave height between 1.5 and 3 m; N3 m: percentage of wave height higher than 3 m. JFM: January, February and March (winter); AMJ: April, May and June(spring); JAS: July, August and September (summer); OND: October, November and December (autumn).

Buoy Location Period of records Statisticalperiod

N Hs (m) Hsmax(m)

σHs (m) Tp (s) Hs b 1.5 (%) 1.5 b Hs b 3 (%) Hs N 3 (%) Storm WEF J/m2

Banyuls 50 m depth 30 mn Annual 97,678 0.67 7.55 0.49 5.80 94.05 5.65 0.30 178,907,10442°29.370′ N 6/3/2002 JFM 22,129 0.84 4.93 0.58 6.19 89.09 9.88 1.0303°10.060′ E AMJ 28,675 0.58 3.54 0.40 5.44 97.20 2.78 0.02

JAS 27,294 0.46 2.08 0.29 5.04 99.51 0.49 0.00OND 19,580 0.89 7.55 0.57 6.86 87.45 12.26 0.30

Leucate 40 m depth 30 mn Annual 104,850 0.65 5.57 0.50 5.76 94.88 4.57 0.55 240,422,44242°55.000′ N 12/16/2006 JFM 24,608 0.77 5.57 0.57 6.08 92.35 6.49 1.163°07.500′ E AMJ 25,536 0.60 3.67 0.43 5.48 96.46 3.47 0.08

JAS 28,229 0.45 2.27 0.27 5.21 99.54 0.46 0.00OND 26,477 0.80 5.47 0.58 6.33 90.72 8.25 1.03

Sète 30 m depth 30 mn Annual 125,546 0.63 5.58 0.53 6.28 93.90 5.16 0.94 266,033,64043°22.290′ N 5/22/2003 JFM 33,721 0.76 5.58 0.63 6.61 91.33 6.90 1.773°46.777′ E AMJ 29,368 0.54 3.67 0.39 6.10 96.53 3.36 0.11

JAS 33,099 0.42 3.35 0.25 5.44 98.04 0.95 0.02OND 29,358 0.82 4.80 0.65 6.87 88.44 9.71 1.85

Espiguette 32 m depth 30 mn Annual 48,917 0.67 4.26 0.50 5.81 93.63 5.95 0.41 233,338,93747°24.660′ N 8/17/2008 JFM 11,362 0.77 4.26 0.57 6.22 91.61 7.37 1.0204°09.750′ E AMJ 13,797 0.58 3.21 0.40 5.68 96.59 3.36 0.04

JAS 13,294 0.50 2.85 0.32 5.24 98.50 1.50 0.00OND 10,464 0.90 3.81 0.61 6.20 85.73 13.50 0.76

178 N. Aleman et al. / Geomorphology 241 (2015) 175–191

period, the number of major storms is much greater, leading to a stan-dard deviation between 0.57 and 0.65 (Table 1). Wave energy also dis-plays spatial variability, especially during storm events, which are moreenergetic in the northern part (Sète station). The southern part of theGulf of Lions is protected from S–SE storms by the rocky Pyreneescoast. Storm waves from the E are shore-normal in the southern partand oblique in the central and northern parts, whereas the most fre-quent SE storm waves are oblique in the southern part and more orless shore-normal in the central and northern parts. Current patterns es-sentially depend on thewave energy and are dominated by a longshorecomponent. Current velocities are close to 0.1m·s−1 during fair weath-er, and can reach 1.0 m·s−1 during storms (Certain et al., 2005a; Ferreret al., 2011; Robin et al., 2014).

3. Methods

3.1. Beach morphology

Beach morphological states and bar types were identified from veryhigh-resolution beach morphological analysis conducted from data ob-tained from a topo-bathymetric LiDAR survey of the Gulf of Lions coastin summer 2009 (DREAL-LR). To determine modal beach states, theLiDAR analysis also draws on previous studies on bar shapes at regionalor local scales that were based on extensive analysis of aerial photo-graphs (Barusseau and Saint-Guily, 1981; Certain, 2002; Michel et al.,2011; Aleman et al., in preparation) and field reconnaissance(Barusseau et al., 1994; Akouango, 1997; Certain, 2002; Certain andBarusseau, 2005; Ferrer et al., 2009; Gervais et al., 2012). LiDARbathym-etry has the advantage of clearly depicting the subaqueous beach mor-phology and the complex bar patterns, especially in the very lowmicrotidal range setting of the Gulf of Lions (Aleman et al., 2011). Thesummer 2009 survey was conducted following several weeks of beachstability and with clear waters, enabling the accurate 3D mapping ofmodal beach states. The LiDAR system consisted of a LADS Mk II, de-ployed on a Dash 8-202 aircraft flying between 360 and 670 m. Thegreen laser (e.g., bathymetric beam) frequency was 900 Hz, for a mini-mal spatial resolution of approximately 5 m and for a 240 m-wideswath. The distance between flight lines was 220 m (with a 20 m over-lap). Transversal flights over the coastline were also conducted every5000 m. The data collected extended from the aeolian dune backingthe beach to 20 m water depth. The total area covered was 300 km2

and included 25 million measured points. Airborne LiDAR systemssuch as LADS MkII are particularly suitable for surveys of large coastalareas such as wide embayments or wide beach corridors (Saye et al.,2005; Shrestha et al., 2005; Montreuil et al., 2014). The LiDAR surveyand post-processing data were carried out by EUROSENSE and FUGROLADS Corporation. Because of the presence of rock outcrops, we carriedout a specific control of the accuracy of the LiDAR data on stable subtidalareas. This highlighted vertical and horizontal accuracies of ±0.30 mand ±2.98 m, respectively (Aleman, 2013).

A Digital Elevation Model (DEM) was established from the LiDARdata using Fledermaüs IVS7 software based on a “Number of Sample”and “Neighbourhood” algorithm. The beach slope, an additional de-scriptor of beach state that is complementary to the parameter Ω (seeSection 3.4), was determined from 161 beach profiles extracted everykilometre from the LiDAR using ArcGIS 10 Spatial Analyst and 3D Ana-lyst tools. The profiles covered the subaerial beach (excluding urbanareas) up to the maximum depth probed by the LiDAR (between −15and −20 m depth). The DEM and profiles were referenced to tidesmonitored at two control points in Port-la-Nouvelle and Port Camargueharbours. Tide data from the French Navy (Service Hydrographique etOcéanographique de la Marine, SHOM) (Port-Vendres, Sète andSaintes-Maries de la Mer) were also used. Technical support and exper-tise on tides, as well as data validation, were provided by SHOM(Vanroye, 2009).

3.2. Sediments

In order to determine the sediment fall velocities used in beach stateclassification on the basis of the parameter Ω, sediment samples werecollected every kilometre in the same locations as those of the profilesgenerated from the LiDAR data. Samples were taken from both the sub-aerial beach surface (1–2 cmdepth) and from the nearshore zone. Near-shore samples were collected with a conical dredge over a depth of5 cm. Six morphological zones were sampled: (1) the upper beach,(2) the active berm at the landward limit of the swash, (3) the beachstep, (4) the crest of the inner bar, (5) the crest of the outer bar whenit was present, and (6) the lower shoreface at around −6 to −8 m,which marked the transition to a more regular and gentler slope thanthat of the bar zone. A total of 805 samples were collected and analysedin the laboratory. Short stretches of rocky coast were excluded fromsampling. The sieving method was chosen because most laboratory

179N. Aleman et al. / Geomorphology 241 (2015) 175–191

grain-size archives have been obtained in this way. The sediment wassifted using an AFNOR column (10, 8, 6.3, 5, 4, 3.15, 2.5, 2, 1.6, 1.25, 1,0.8, 0.63, 0.5, 0.4, 0.315, 0.25, 0.2, 0.1, 0.08, 0.063, 0.05 mm) withcorrected mesh to obtain the median particle size. The average medianparticle sizes of the subaerial (upper beach, berm and beach step) andsubtidal zones (inner bar, outer bar and lower shoreface) were comput-ed. The fall velocity was calculated using the equation of Gibbs et al.(1971).

3.3. Wave parameters

Hydrodynamic data (significant wave height (Hs) and peak period(Tp)) were obtained and analysed by statistical methods from the fourstations along the Gulf of Lions coast (Fig. 1B). Offshore-directedwaves are associated with offshore winds and are not relevant to thework we present here. Data on waves approaching from 225 to 315°for the southern part and 315 to 45° for the northern part were

Fig. 3. Longshore distribution ofmedian grain size for 9 sample locations: upper beach, berm, beaverage.

discarded. Each part of coast was linked to a buoy, the choice guidedby coastal morphology and orientation, and by previous wave climatestudies in the Gulf of Lions (Guizien, 2009; Gervais, 2012; Aleman,2013). Finally, offshore wave data were used to characterise breakingconditions. Several expressions for computing breaking wave height(Hb) with different slopes (upper-shoreface, lower shoreface and entireshoreface described in the paragraph 2) were tested by comparisonwith in situ wave data in the breaker zone (Certain et al., 2005a;Gervais, 2012; Robin et al., 2014). The relationship proposed bySunamura and Horikawa (1974) yielded the best results when the en-tire shoreface slope is considered, and was used to compute Hb fromthe offshore wave conditions:

Hb

H0¼ tanβð Þ0:2 H0

L0

� �−0:25

whereHb is the breaker height,H0 and L0 are respectively the deepwaterwave height and length, and tanβ is the gradient of the entire shoreface.

ach step, inner bar, outer bar, lower shoreface, subaerial average, subtidal average and total

180 N. Aleman et al. / Geomorphology 241 (2015) 175–191

3.4. The parameter Ω

The parameter Ωwas computed to predict the beach morphologicalstate. Wright et al. (1985) showed that beach morphology may be bestrelated to average, rather than instantaneous, wave conditions. Under alow-energy context, such as along the Gulf of Lions, the morphologicalresponse is more sensitive to changing wave conditions (Hegge et al.,1996; Jackson et al., 2002; Costas et al., 2005; Gómez-Pujol et al.,2007). As a result, time averages representative of annual, seasonal,monthly and storm wave conditions have been calculated to establishthe parameter. In order to ascertain the effects of cross-shore sedimentgradation, several sample positions on the beach profile (upper beach,berm, beach step, inner bar, outer bar and lower shoreface) wereused. Moreover, using LiDAR data and employing the beach model ofWright and Short (1984), (Aleman et al., 2011) conducted a field classi-fication that recognized the following bar types: (Reflective (R),Longshore Bar-Trough (LBT), Rhythmic Bar and Beach (RBB), Trans-verse Bar and Rip (TBR), Low Tide Terrace (LTT) and Dissipative (D)).The hierarchical classification framework proposed by Loureiro et al.(2013)was then used to obtain field values ofΩ for all profiles surveyedevery kilometre. Assessment of Ωwas then based on comparison of thefield classification with predicted beach states.

4. Results

4.1. Sediments

The superficial sediment of the aerial beach and the shoreface con-sists of well-sorted sand according to Folk andWard (1957). Regionally,the sediments become gradually finer-grained northward. This impliesa difference between the southern part (medium-coarse sands, 0.25–1.60 mm), where the influence of river sand supply is appreciable, andthe central and northern parts located updrift of rivermouths (fine-me-dium sands 0.17mm) (Fig. 3). The boundary between these two parts isrepresented by Cape Leucate (Fig. 3), which corresponds to a semi-impermeable barrier to longshore sediment transport (Brunel et al.,2014). Longshore variations can be also observed on a smaller scale,for example in the vicinity of river mouths or in front of nearshorerock flats. The sediment of the beach step shows greater heterogeneitythan that of other units of the system, such as the berm and nearshorezone (Fig. 3).

Fig. 4. Longshore evolution of the D50 standard deviation for the 6 sediment sample loca

Cross-shore variations are observed with an overall seaward de-crease in grain size. This variation is greater in the southern and north-ern parts of the study area where the standard deviation between eachbox of the system is higher (Fig. 4). In contrast, the lower shoreface isvery homogeneous on a regional scale (Fig. 3). On the adjacent shelf,sediment is mostly composed of sand down to 25–30 m water depth,and muddy silts between 25 and 40 m depth (Aloïsi et al., 1973).

4.2. Beach states

Six beach states were identified (Fig. 5) based on bar types, profileshape (Fig. 6), sediment characteristics and local specificities of theshoreface. Several beach state sectors were thus identified along the200 km of coastline (Fig. 7). Specific attention was paid to the inter-class transitions (Fig. 8).

4.2.1. Reflective beachesReflective beaches are characterised by an abrupt profile and the ab-

sence of nearshore bars (Aleman et al., 2011) (Figs. 5, 6). This beachstate (5 profiles, 1.1% of the coastline, Fig. 7) is only observed in a con-fined cell in the southern part of the study area between the rocky Pyr-enees coast and harbour jetties to the north (Fig. 8A). The sediment iscoarse on the subaerial beach (D50 = 1.86 mm) and medium in thesubtidal zone (D50 = 0.91 mm) (Fig. 5). The slope is steep and attainsan average of 1.16° (Fig. 5).

4.2.2. Intermediate beachesAn intermediate beach state occurs only in the southern part (47

profiles, 23.6% of the coastline) (Fig. 7). The southern boundary withthe reflective beach zone is represented by the harbour jetties men-tioned above (Fig. 8A). To the north, this beach state is limited byCape Leucate (Fig. 8B). Intermediate beaches exhibit well-developedRBB to TBR morphology. Usually two bars are present but an additionalbar close to the beach is also sometimes observed. This bar displayscomplex features with a long platform that extends along the beach,and small transverse bars that can occasionally be connected with theinner bar (Aleman et al., 2011) (Fig. 5). The median grain size is coarsein the subaerial zone (D50 = 1.03 mm) and fine in the subtidal zone(D50 = 0.27 mm) (Fig. 5). The slope of the beach is 0.87° on average(Figs. 5, 6).

tion sites (upper beach, berm, beach step, inner bar, outer bar and lower shoreface).

181N. Aleman et al. / Geomorphology 241 (2015) 175–191

4.2.3. Intermediate–dissipative beachesIntermediate–dissipative beaches are observed at thewestern endof

the northern part (11 profiles, 10.3% of the coastline, Fig. 7) betweenCapes Agde andMont Saint Clair (Fig. 8C, D), aswell as along Espiguettespit (11 profiles, 6.9% of the coastline), where the boundary consists ofharbour jetties (Fig. 8F). This beach state is characterised by an innerRBB and an outer LBT that grades to D (Aleman et al., 2011) (Fig. 5).The sediment is composed of fine sand in the subtidal zone (D50 =

Fig. 5. 3D classification of beaches observed in the Gulf of Lions. (a) Shoreface slope, (b) medianshape, (e) location of beach state compartment, (f) number of profiles, (g) kilometres of coast

0.18 mm) and medium sand in the subaerial zone (D50 = 1.10 mm)(Fig. 5) and the slope is lower with an average of 0.5° (Figs. 5, 6).

4.2.4. Dissipative beachesDissipative beaches are found between Capes Leucate (Fig. 8B) and

Agde (Fig. 8C). They exhibit two straight bars in a transition from LBTto D. Another small bar can be observed close to the shore (Alemanet al., 2011) (Fig. 5). The beach profile differs from the previous one

particle size of sediment from subaerial and subtidal areas, (c) number of bars, (d) profileand (h) percentage of coast.

182 N. Aleman et al. / Geomorphology 241 (2015) 175–191

by a break in slope at−15 m, evolving into a convex configuration off-shore (Figs. 5, 6). Subtidal sand is fine with a D50 of 0.18 mm, whereassupratidal sand is medium (D50 = 0.77 mm) (Fig. 5). The averageslope is 0.48° (1.03° for the steepest offshore profile). These states pre-vail only in the central part (54 profiles, 25.4% of the coastline, Fig. 7).

4.2.5. Rock platform-constrained beachesBeaches constrained by a rock platform are a sediment-poor beach

typemarked by the presence of a substratum composed of bedrock out-crops on the shoreface. For the Aresquiers area (7 profiles, 5.7% of thecoastline, Fig. 1), the substratum appears between−3 and−5 m over-lain by a very thin sand sheet forming the beach. These beaches exhibitone or two straight bars with a slightly more marked outer bar. Thegrain size consists of subaerial coarse sand (D50 = 1.91 mm) andsubtidal fine sand (D50 = 0.14 mm) (Fig. 5). The slope of the sand sys-tem is 0.44° (Fig. 5). In the Palavas-Maguelone area (14 profiles, 13.8%of the coastline, Fig. 1), the substratum appears at a depth of −10 m

Fig. 6. All beach profiles extracted from LiDAR data and selected profiles repres

(Figs. 5, 6). The sand sheet is thus thicker than in the previous zone.The subaerial grain size is slightly finer (D50 = 1.17 mm) (Fig. 5) andthe beach gradient larger (0.56°) than in the Aresquiers area (Figs. 5,6). The two areas of beach constrained by a rock platform are contigu-ous and the transition occurs progressively over 2 km. The southernboundary corresponds to Mont Saint Clair (Fig. 8D), whereas the north-ern boundary with the non-barred dissipative beaches is gradual over afew kilometres, despite the presence of several coastal engineeringstructures (Fig. 8E).

4.2.6. Non-barred dissipative beachesNon-barred dissipative beaches are only observed in the inner part

of AiguesMortes Bay (4 profiles, 0.6% of the coastline, Fig. 7). The south-ern boundary is gradual (Fig. 8E) whereas the northern limit with theintermediate–dissipative beaches corresponds to the jetty on Espiguettespit (Fig. 8F). These beaches exhibit a characteristic concave profile,with the gentlest slope (0.37°) (Figs. 5, 6) and the finest grain size

enting the different morphological compartments (see Fig. 7 for location).

183N. Aleman et al. / Geomorphology 241 (2015) 175–191

(0.34mm(subaerial area) and 0.15mm(subtidal area)) throughout thestudy area (Fig. 5). This state does not usually display bars, but a singlebar is occasionally observed (Figs. 5, 6).

4.2.7. Spatial transitions and extension of beach class compartmentsThe longshore transition between each beach class compartment is

generally abrupt and limits are often formed by rock headlands(Figs. 8B, C, D, F, and Table 2). Only at the reflective-intermediate tran-sition does a harbour constitute the boundary (Fig. 8A). However, thistransition was already apparent on aerial photographs taken beforethe construction of the harbour (Fig. 9). In no other situation do har-bours and coastal defences exhibit a relationshipwith beach class distri-bution (Figs. 8G, H). Beach state compartments extend from a fewkilometres to several tens of kilometres (Fig. 7). Transitions are markedby a change in slope and sediment size, except between intermediate–dissipative and dissipative compartments (Table 2). The number of oc-currences and percentages of the occurrence of each observed beachstate are shown in Fig. 5. In the Gulf of Lions, the most frequent beachstate is the dissipative state (25.9%), followed by the intermediate, therock platform-constrained and intermediate–dissipative states, with re-spectively 23.6%, 19.5% and 17.2% of occurrence.

4.2.8. Modal beach statesComparison of the results of this study with previous research em-

phasizes that beach states are spatially persistent at the multi-decadalscale. Only the bar types may evolve from one stage to another overthe short term in response to seasonal variability in waveconditions. Fig. 10 shows three aerial photographs taken in 1968, 1986and 2009, depicting the intermediate and dissipative compartment.Thanks to the transparency of the water, these pictures show beachstates that have remained the same over the 41-year period. Thus, the

Fig. 7. Location of morphological compartme

2009 LiDAR provides a reliable larger-spatial scale overview of themodal beach states.

4.3. Observed versus predicted beach states

Beach states in the study area were predicted using the parameterΩbased on various combinations of wave statistics (mean annual, meanspring/summer/autumn/winter, storm, August 2009) and grain settlingvelocities (upper beach, berm, beach step, inner bar, outer bar, lowershoreface, overall mean, subaerial mean, subtidal mean) for all LiDARprofiles (every kilometre). Fig. 11 shows, for instance, the mean Ωvalues obtained for combinations of wave climate and settling velocitiesfor the six beach state compartments. The grey background indicatesthe bar types observed on the LiDAR bathymetry (Aleman et al., 2011)and converted to Ω values according to the Wright and Short (1984)model using the hierarchical classification proposed by Loureiro et al.(2013). The results show a large spread of Ω. The best predictions arethose derived from the use of subtidal grain sizewhile storm conditionsyield poor results.

The ratios between observed values of the parameter Ω (Ωo) andpredicted values (Ωp) for the various beach states (Reflective, Interme-diate and Dissipative) are shown in Table 3. The use of upper beach,berm, beach step, lower shoreface and subaerial settling velocitiesyields poor Ωo/Ωp correlations (b50%), except for the berm with stormconditions (60.7%). Better ratios are associated with inner or outer barcrest settling velocities (between 63.1 and 77.0%), with the subtidalmean (72.3 to 84.5%) and with the overall mean (51.3 to 60.7%). Abest ratio (84.5%) is obtained using the subtidal mean settling velocitywith meanwave conditions over a period of onemonth prior to the ob-served beach states (August 2009, Table 3). Storm conditions yield lowratios except with berm settling velocities (60.7%, Table 3).

nts identified on the Gulf of Lions coast.

184 N. Aleman et al. / Geomorphology 241 (2015) 175–191

Ratios of Ωo/Ωp for bar types as defined by Aleman et al. (2011) areshown in Table 4. Ωo/Ωp correlations for bar types are weaker than forbeach states. Ωp values computed with settling velocities on the upperbeach, berm, beach step and lower shoreface are poorly related to Ωo

(x` 4). Better correlations are obtained with settling velocities fromthe inner or outer bars, especially for autumn and winter waves (60.3to 64.8%). The best Ω predictions, with a ratio of 72.3%, are obtainedusing the mean settling velocity of the subtidal zone and the mean an-nual wave conditions prior to the observation of bar types. As in thecase of beach states, storm conditions yield poor results (b50%), what-ever the sediment fall velocity used.

Fig. 12 shows the spatial variability of Ω for combinations of settlingvelocities and wave conditions that yield the best correlations with theobserved morphologies. The observed bar types are represented by agrey background. The results show large fluctuations in Ωp over shortdistances, especially in the intermediate and dissipative domains. Thegeneral trend is consistent with the observed bar types, with a progres-sive upstate shift from the reflective to the dissipative domain (Fig. 12).However, for inner bar sediments, Ωp tends to underestimate the ob-served morphologies in the intermediate area. The best predictionsare obtained for the reflective and dissipative fields (Fig. 12). For outerbar sediments, better predictions are obtained for the intermediate do-main, but the bar types of the intermediate–dissipative domain are

Fig. 8. LiDAR bathymetry of beach state transitions: (A) reflective to intermediate, (B) intermedform-constrained, (E) rock platform-constrained to non-barred dissipative, and (F) non-barreenced by (G) harbours or (H) coastal defences.

over-estimated (Fig. 12). Finally, the best predictions are obtainedwhen the mean subtidal sediment fall velocities are combined with an-nual or August 2009wave conditions. In this case, themajority of differ-ences between Ωo/Ωp are located in the intermediate field with a slightunder-estimation of the observed morphologies (Fig. 12).

5. Discussion

The discussion will focus on the following selected themes that bearon the identification and characterisation of beach states and their tran-sitions, on environmental factors influencing beach states and their dis-tribution, and finally on the validity, skill and limitations of theparameter Ω in characterising and these predicting beach states, basedon the exhaustive analysis of combined LiDAR bathymetry data, grainsettling velocities and wave data.

5.1. Beach state classification and state transitions

Wright and Short (1984) developed a single-bar beach model forwave-dominated coasts from Australian beaches with six beach states.This model was complemented by Short and Aagaard (1993) who ex-tended it to two- to three-bar systems. Previous studies on Gulf ofLions beaches have enabled the description of complex bar types and

iate to dissipative, (C) dissipative to intermediate–dissipative, (D) dissipative to rock plat-d dissipative to intermediate–dissipative. The last two images show beach states uninflu-

Table 2Characteristics of the boundaries and variations in sediment particle size and slope between each morphological compartment.

Transition Boundaries Particle size Slope

Reflective to intermediate Harbour/progressive Decreases DecreasesIntermediate to intermediate–dissipative Cape Decreases DecreasesIntermediate–dissipative to dissipative Cape Constant ConstantDissipative to rock platform-constrained Cape Increases IncreasesRock platform-constrained to non-barred dissipative Progressive Decreases DecreasesNon-barred dissipative to dissipative Harbour Increases Increases

185N. Aleman et al. / Geomorphology 241 (2015) 175–191

transitions over short distances (Ferrer et al., 2009; Aleman et al., 2011),the latter study based on high-resolution 3D LiDAR data. Using a combi-nation of aerial photograph data and such 3D data, the present studyshows, on a regional scale, a large diversity of beach states with severalmorphologically continuous states over several kilometres. Althoughthe bars exhibit complex morphology and 3D patterns, they are classicsystems with respect to the literature, both from a morphological (thisstudy) and a dynamic point of view (Aleman et al., 2013).

The associated beach states show, however, two unusual or atypicaltypes. First, the rock platform-constrained state (which represents a sig-nificant 19.5% of the coastline) presents a steep beachface fronting bed-rock that can nourish the berm with pebbles and cobbles. This state istypical of sediment-starved settings with a thin and narrow sandsheet overlying the bedrock. Despite this, one or two straight bars arepresent in the confined nearshore sandy zone. These bars are smalland migrate offshore at a very slow rate (19 m·year−1) (Alemanet al., 2013). The rock platform is wide and shallow (−10 m) and bed-rock attenuation of strongerwaves offshore could explain this relativelyslow migration (e.g., Anfuso et al., 2003; Sabatier et al., 2004). Beacheswith bedrock/rock flats/reef are common in the literature (Sanderson

Fig. 9. 1972 and 2009 aerial photographs (©IGN) of the reflective to intermediate transitio

and Eliot, 1999; Short, 2006), but the presence of bars has, to our knowl-edge, never been documented. This relatively common beach state onthe Gulf of Lions coast is morphologically original, as is the controlexerted by bedrock on bars and beachface characteristics. Secondly, de-spite the fact that the non-barred dissipative state is represented in onlya short part of the coast (6 km or about 3% of the studied coast), thisstate is unusual in a microtidal environment. This concave featurelessprofile has been mainly identified in meso- and macro-tidal environ-ments (e.g., Short, 2006; Scott et al., 2011). However, it can also occurin sheltered, microtidal, low wave-energy settings characterised byfine sand (Klein et al., 2010), as observed in Aigues Mortes Bay in thenorthern part of the study area. As reported by Hegge et al. (1996), sed-iment size apparently determines the morphodynamic state of thebeach under sheltered low-energy conditions where the wave fetch isrestricted.

Alongshore transitions between bar-beach states, as mentioned inthe Introduction, have been rather poorly documented. The use of 3Dtopo-bathymetric LiDAR in the present study over a regional and almostcontinuous sandy beach that is nearly 200 km long allows us to bringout these transitions, regarding which this study has highlighted a

n showing the existence of the transition prior to the construction of Argelès harbour.

186 N. Aleman et al. / Geomorphology 241 (2015) 175–191

number of characteristics: (1) the spatial extent of beach class cellsvaries from a few kilometres to several tens of kilometres; (2) sometransitions are sudden when the beaches are interrupted by largerocky headlands (Cap Leucate, Cap d'Agde,Mont Saint Clair); (3) transi-tions can also occur progressively over a fewhundredmetres on contin-uous beaches, and in these cases, bar patterns are complex andentangled (Aleman et al., 2011); (4) harbours and river mouths do notseem to disturb the regional beach state pattern. Transitions are thus es-sentially under the overarching influence of geology.

5.2. The influence of the geological framework

Beach state models do not take into account the influence of geolog-ical constraints such as headlands, rock outcrops and reefs on the

Fig. 10. Aerial photographs (©IGN) taken in 1968, 1986 and 2009 in the intermediate and disbeach states have remained the same over the 41 years spanned by the photographs.

morphodynamics that underlie such classifications. However, severalrecent studies have emphasized the importance of these constraints,some of which can have an overarching influence on observed beachmorphologies (e.g., Jackson et al., 2005; Short, 2006; Jackson andCooper, 2009; Scott et al., 2011; Loureiro et al., 2013).

First, the presence of capes plays a role in sediment cell size andtransport (Sanderson and Eliot, 1999), rip formation (Short, 1999,2006, 2010), and wave propagation (Gómez-Pujol et al., 2007). It isshown in this study that headlands/capes along the Gulf of Lions coaststrongly influence beach and shoreface characteristics. The most clearlyexpressed influence is observed at Cape Leucate, which forms theboundary between intermediate (southward) and dissipative (north-ward) beach states, notably through its influence on grain size on eitherside of the cape. The Cape interrupts medium-coarse sand from the

sipative compartment. Thanks to the transparency of the water, these pictures show that

187N. Aleman et al. / Geomorphology 241 (2015) 175–191

coastal rivers transported northward by longshore drift, favouring larg-er fall velocities and intermediate beach states. Fine sand has beenshown to be trapped beyond the wave closure depth at about −6 to−8 m (Brunel et al., 2014). However, only a small fraction of this finesediment can pass through the outer bar and supply the intermediatebeaches. Sand transported north of the Capemostly comes from ancientabandoned lobes of the Rhône delta, located further north, outside thestudy area, and consists of fine tomedium sand associatedwith dissipa-tive beaches. Another example on the Gulf of Lions coast is that of therock outcrops north of Mont Saint Clair headland, characterised by therock platform-constrained beach state (Fig. 5). The influence of Caped'Agde is more complex but it clearly forms a boundary between dissi-pative (southward) and intermediate–dissipative (northward) beachstates.

Secondly, accommodation space, largely controlled also by thegeological context, can have a significant impact on shorefacemorphodynamics and observed beach states, as shown in this studyand in the literature. Bedrock outcrops not only generate wave energydissipation (Sanderson and Eliot, 1999; Short, 2006, 2010; Short andWoodroffe, 2009; Muñoz-Perez and Medina, 2010), the emergence oftopographic rips (Short, 1999, 2006, 2010), but also influence and con-strain sediment accommodation space and the sediment stock (Jacksonet al., 2005; Scott et al., 2007, 2011; Jackson and Cooper, 2009). In thenorthern area, in the rock platform-constrained beach compartment,

Fig. 11. Cross-shore variation ofmeanΩ values calculated for wave conditions associatedwith vto observedbeach state. UB: upper beach, B: berm, BS: beach step, IB: inner bar, OB: outer bar, LSaverage.

where the depth of closure is between −6 and −10 m (Sabatier et al.,2004), the substratum crops out at a depth of −5 to−10 m, sedimentsupply is limited (Certain et al., 2005b), full seaward development of theprofile is not possible, and the profile characteristics are modified.Moreover, the bedrock modulates wave energy and limits the onshoretransfer of sand between the upper and lower shorefaces. Sedimenttransfers are observed only during high-energy events, but the inputconsists of coarse sand or pebbles. This geological context favoursshorter and steeper beaches.

5.3. The influence of multi-decadal sediment budget changes on beachstates: a tentative approach

Net long-term beach and nearshore sediment budget changes, suchas those that have been reported in the Gulf of Lions, may presumablyaffect long-term beach states and their distributions. If we address thespecific problem of multi-decadal sediment budget changes, theshoreface sediment deficit in the western Gulf of Lions over the last25 years (Brunel et al., 2014) has, apparently, not affected the spatialand temporal persistence of beach states identified in this study. Thistheme certainly offers scope, however, for further study as climatechange and increasing anthropogenic pressures are likely to exacerbatethe beach and shoreface sediment budget in the Gulf of Lions in the

arious seasons or events for the six beach state compartments. Greyscale area corresponds: lower shoreface, Sup.: supratidal average, Sub.: subtidal average, andAv.: all environment

Table 3Percentage of beach state ratios of Ωpredicted/Ωobserved (reflective, intermediate, and dissipative) for all sediment sample locations and hydrodynamic periods.

Annual Winter Spring Summer Autumn August 2009 Storm (Hs > 2m) N sample

Upper beach 47.7 47.7 46.6 43.2 47.7 42.0 44.3 88

Berm 44.8 47.6 44.1 37.2 49.7 36.6 60.7 145 %

Beach step 33.6 39.2 32.9 30.1 38.5 28.7 41.3 143 <10

Inner bar 73.3 75.3 71.9 67.1 74.0 65.1 58.2 146 10–25

Outer bar 64.8 67.2 71.3 76.2 63.1 77.0 45.1 122 25–50

Lower shoreface 31.8 31.1 37.2 37.8 30.4 43.2 27.0 148 50–60

Supra. av. 34.7 35.4 34.0 31.3 37.5 30.6 42.4 144 >60

Sub. av. 77.0 72.3 77.0 83.8 70.3 84.5 43.9 148

Total av. 60.7 60.7 58.7 51.3 58.7 52.7 56.0 150

Se

dim

en

t sa

mp

les

Hydrodynamic conditions

188 N. Aleman et al. / Geomorphology 241 (2015) 175–191

coming decades, paving theway for disequilibrium in the currently pre-vailing beach state patterns and their longshore distributions.

It is interesting to note that the theme of the effect of net and persis-tent sediment budget changes on beach states was tentatively and con-ceptually approached byWright et al. (1985) using a long-term form ofdisequilibrium index based on the parameter Ω, which is furtherdiscussed in Section 5.5. This disequilibrium index approach regardinglong-term beach state morphodynamic switches liable to be caused bymulti-decadal sediment budget variations has, in fact, hardly been fur-ther explored in the literature. In a nutshell and following Wrightet al. (1985), long-term beach state changes can be induced by environ-mental parameters acting on Hb that engender disequilibrium by ren-dering the system more dissipative or more reflective. Among theselong-term parameters are sea-level rise, net sediment budget changesor net changes in wave climate such as increased storminess, environ-mental conditions that are currently causes for concern as far as beachstability goes.

5.4. The influence of coastal engineering structures

Regarding harbours in this study, only one of twelve constitutes aboundary between the reflective and intermediate states. However,

Table 4Percentages of bar type ratios of Ωpredicted/Ωobserved (R, LTT, TBR, RBB and D) for all sedime

Annual Winter Spring Summer A

Upper beach 13.6 20.5 5.7 3.4 2

Berm 5.5 11.0 4.8 1.4 1

Beach step 9.8 16.1 7.0 2.1 1

Inner bar 52.1 52.1 41.1 21.2 6

Outer bar 53.3 64.8 54.9 43.4 6

Lower shoreface 29.7 28.4 31.8 39.9 3

Supra. av. 6.9 9.7 4.2 2.1 1

Sub. av. 72.3 69.6 66.2 62.8 6

Total av. 24.7 30.7 22.0 10.0 3

Se

dim

en

t sa

mp

les

Hydrodynamic condi

the aerial photographs prior to, and followingharbour construction sug-gest that this transition has always been in existence and is not directlyrelated to the vicinity of the harbour (Fig. 9). The 3D subaqueous LiDARdata suggest that harbour breakwaters have very local impacts on barmorphology where such bars are intersected by jetties, and generateonly slight changes in the subaqueous slope. Smaller coastal structuressuch as groynes do not seem to affect the regional/local beach state.Their impact is generally limited to highly localized rips and complexbar patterns in shallowwater (Aleman et al., 2011). The impact of engi-neering structures in themicrotidalMediterranean setting of the Gulf ofLions appears to be much lower than that of the modally high-energy,storm-dominated coast of the Netherlands (Short, 1992).

5.5. The Ω parameter

Important discrepancies have been observed between the predictedand observed beach morphologies using the Ω as a descriptor of beachstates (Wright et al., 1987; Sanderson and Eliot, 1999; Benaventeset al., 2000; Klein and Menezes, 2001; Thieler et al., 2001; Jacksonet al., 2005; Loureiro et al., 2013). However, in these cited examplesthe authors concluded on the usefulness of this parameter in discrimi-nating between the extreme reflective and dissipative beach states,

nt sample locations and hydrodynamic periods.

utumn August 2009 Storm (Hs > 2m) N sample

6.1 3.4 11.4 88

5.9 2.1 36.6 145 %

6.8 0.7 24.5 143 <10

0.3 22.6 39.0 146 10–25

0.7 42.6 36.9 122 25–50

1.1 41.9 2.7 148 50–60

6.0 1.4 23.6 144 >60

6.9 67.6 37.8 148

1.3 8.7 29.3 150

tions

189N. Aleman et al. / Geomorphology 241 (2015) 175–191

but on its inadequacy in characterising intermediate states (Wrightet al., 1987; Bauer and Greenwood, 1988; Ranasinghe et al., 2004;Grasso et al., 2009; Scott et al., 2011; Loureiro et al., 2013).

Marked variability can occur in calculated Ω values depending onthe sediment sample location across the active profile, as shown byBenedet et al. (2004a). The poor Ωo/Ωp correlations with subaerial set-tling velocities in a microtidal context may be due, for instance, to theinfrequent inundation of the upper beach which is affected by surge

Fig. 12. Longshore variation of theΩ parameter that yields the best correlations with the observwave conditions, and (C) average subtidal sediment sampleswith annual and August 2009wave(2) intermediate, (3) intermediate–dissipative, (4) dissipative, (5) rock platform-constrained,

that can cause pronounced differences in sediment characteristics be-tween the subaerial and subtidal beach, as reported by Jackson et al.(2002). Subtidal environments such as the inner bar, outer bar andlower shoreface tend to yield more realistic values. Gómez-Pujol et al.(2007) noticed that the parameterΩ could be useful in gross beach clas-sification but tends to fail with seasonal variability. Jiménez et al. (2008)emphasized the importance of taking into account the duration and in-tensity of the wave forcing necessary for the morphological reaction of

edmorphologies: (A) inner and (B) outer bar sediment samples with annual and autumnconditions. The circled numbers indicate themorphological compartments: (1) reflective,

(6) non-barred dissipative and (7) dissipative.

190 N. Aleman et al. / Geomorphology 241 (2015) 175–191

the beachface. Our results show that the best predictions for beachstates (Reflective, Intermediate and Dissipative domains) were obtain-ed using the mean settling fall velocity of the subtidal sedimentscoupled to wave statistics one month prior to the field observation(Ωo/Ωp of 84.5%). When only storm waves (Hs N 2 m) are consideredas the most relevant in generating morphological change, theΩpredicted/Ωobserved ratios become very low, and only ratios for theberm are reasonably good (Ωo/Ωp of 60.7%). Ω is less skillful inpredicting bar types (Intermediate domain). However, a reasonablygood success rate of 72.3% is achieved when the subtidal sediment fallvelocity is coupled to mean annual hydrodynamic conditions.

The results presented here highlight the relatively good skill of Ω inpredicting large-scale variations in mean beach state, and provide veryclear guidance on the importance of sediment sampling strategies andhy-drodynamic conditions that need to be taken into account in a microtidalenvironment. Ω appears to be a better descriptor of beach states that donot change at themulti-decadal scale, thanof bar types that canbe subjectto very dynamic short-term changes in the Gulf of Lions (Ferrer et al.,2009; Gervais et al., 2011), as in other low wave-energy environments(Hegge et al., 1996; Jackson et al., 2002; Costas et al., 2005;Gómez-Pujol et al., 2007). Thus, as aptly stated by Scott et al. (2011):“Beach classification models based on environmental parameters are, bynecessity, simplifications and should be used as tools for understandingmorphodynamic systems, rather than beach type prediction”.

6. Conclusion

Nearly 200 km of sandy beach morphological state in the Gulf ofLionswere investigated using 2D and 3Dmorphological surveying, sed-imentological sampling and hydrodynamic monitoring. The large newdata set obtained has enabled an analysis of the configuration and orga-nization of the beach and associated bar types. It complements a priorreview of the bar patterns in this microtidal Mediterranean setting(Aleman et al., 2011).

Our results show that:

– classical beach states described in the literature are present over the200 km of the studied coast. Nevertheless, two unusual beach statesfor a microtidal environment (non-barred dissipative beaches andbeaches constrained by a rock platform) are observed in relation tospecific local environmental conditions.

– beach state transitions may be abrupt, depending on the distur-bances caused by rock headlands, or continuous as a function ofthe alongshore sedimentary and hydrodynamic conditions.

– the geological framework has an important control on beach state.This control can be expressed by the presence of rocky barriersthat are semi-impermeable to sediment transport (generating sig-nificant variability and sediment size families) or by the formationof sheltered beaches. In some compartments, bedrock outcropslimit accommodation space and lead to a decrease in wave energy.A net sediment deficit in the shoreface of the study area over thelast 25 years has not affected the spatial and temporal persistenceof identified beach states and bar types.

– the influence of harbours and smaller cross-shore engineering struc-tures on the beach states appears to be insignificant.

– the parameter Ω has a level of predictive accuracy that strongly de-pends on specific wave periods and sediment sample locations. Pre-diction is best using average subtidal sediment statistics coupled tohydrodynamic conditions one month prior to observed beach statein a low-energy microtidal environment.

Acknowledgements

We thank theDREAL-LR for their financial support and the provisionof LiDAR and hydrodynamic data. We also thank the entire team of theLITTOSIS campaign and the following people for the data collection in

the course of previous field studies: P. Barthe, O. Beaurepaire, L. Cance,F. Carol, D. Cervello, C. Menniti, C. Michel, O. Raynal, R. Rivoal and M.Salvat. Frederic Bouchette (Géosciences-Montpellier), Heloïse Michaud(SHOM), two anonymous reviewers and Editor A. J. Plater for their help-ful and constructive comments on the original draft. We acknowledgeGLADYS (www.gladys-littoral.org) and SO LTC (www.soltc.org) re-search groups in regards to material support.

References

Akouango, E., 1997. Morphodynamique et dynamique sédimentaire dans le golfe du Lion.(Phd Thesis). Contribution à l'étude de la zone côtière dans l'actuel et le quaternairerécent. University of Perpignan, p. 191.

Aleman, N., 2013. Morphodynamique à l'échelle régionale d'une avant-côte microtidale àbarres sédimentaires: le cas du Languedoc-Roussillon à l'aide de la technologie LiDAR.(PhD Thesis). University of Perpignan, p. 251.

Aleman, N., Certain, R., Robin, N., Barusseau, J.-P., 2015n. Influence of the Morphodynamic ofSlightly Oblique Nearshore Bars on the Net Offshore Bar Migration (in preparation).

Aleman, N., Robin, N., Certain, R., Barusseau, J.P., Gervais, M., 2013. Net offshore bar migra-tion variability at a regional scale: inter-site comparison (Languedoc-Roussillon,France). J. Coast. Res. (SI65), 1715–1720.

Aleman, N., Robin, N., Certain, R., Vanroye, C., Barusseau, J.P., Bouchette, F., 2011. Typologyof nearshore bars in the Gulf of Lions (France) using LIDAR technology. J. Coast. Res.(SI64), 721–725.

Aloïsi, J.C., Got, H., Monaco, A., 1973. Carte géologique du précontinent languedocien au 1/250000ème, in: International Institute for Aerial Survey and Earth Sciences (I.T.C.),Netherlands.

Anfuso, G., Martinez del Pozo, J.A., Gracia, F.J., Lopez-Aguayo, F., 2003. Long-shore distri-bution of morphodynamic beach states along an apparently homogeneous coast inSW Spain. J. Coast. Conserv. 9, 49–56.

Anthony, E.J., 1998. Sediment-wave parametric characterization of beaches. J. Coast. Res.14 (1), 347–352.

Backstrom, J.T., Jackson, D.W.T., Cooper, J.A.G., 2009. Contemporary morphodynamics of ahigh-energy headland-embayment shoreface. Cont. Shelf Res. 29 (11-12), 1361–1372.

Barusseau, J.-P., Radulescu, M., Descamps, C., Akouango, E., Gerbe, A., 1994.Morphosedimentary multiyear changes on a barred coast (Gulf of Lions, Mediterra-nean Sea, France). Mar. Geol. 122 (1–2), 47–62.

Barusseau, J.-P., Saint-Guily, B., 1981. Disposition, caractères et mode de formation desbarres d'avant-côte festonnées du littoral du Languedoc-Roussillon (France). Oceanol.Acta 4 (3), 297–304.

Battjes, J.A., 1974. Surf similarity. 14th Conference on Coastal Engineering, pp. 466–480.Bauer, B.O., Greenwood, B., 1988. Surf-zone similarity. Geogr. Rev. 78, 137–147.Benaventes, J., Gracia, F.J., López-Aguayo, F., 2000. Empirical model of morphodynamic

beachface behaviour for low-energy mesotidal environments. Mar. Geol. 167 (3–4),375–390.

Benedet, L., Finkl, C.W., Campbell, T., Klein, A., 2004a. Predicting the effect of beach nour-ishment and cross-shore sediment variation on beach morphodynamic assessment.Coast. Eng. 51 (8–9), 839–861.

Benedet, L., Finkl, C.W., Klein, A.H.F., 2004b. Morphodynamic classification of beaches onthe atlantic coast of florida: geographical variability of beach types, beach safety andcoastal hazards. J. Coast. Res. (SI39), 360–365.

Brunel, C., Certain, R., Sabatier, F., Robin, N., Barusseau, J.P., Aleman, N., Raynal, O., 2014.20th century sediment budget trends on the Western Gulf of Lions shoreface(France): an application of an integrated method for the study of sediment coastalreservoirs. Geomorphology 204, 625–637.

Calliari, L.J., Klein, A.H.F., Barros, F.C.R., 1996. Beach differentiation along the Rio Grandedo Sul coastline (southern Brazil). Rev. Chil. Hist. Nat. 69, 485–493.

Casanobe, G., 1961. Pluviométrie, annales climatologiques. Chambre d'agriculture desP.O.pp. 11–16.

Castelle, B., Bonneton, P., Dupuis, H., Sénéchal, N., 2007. Double bar beach dynamics onthe high-energy meso-macrotidal French Aquitanian Coast: a review. Mar. Geol.245 (1–4), 141–159.

Cattaliotti-Valdina, D., 1978. Modalités et bilan de la sédimentation pélitique dans un mi-lieu margino-littoral: le complexe lagunaire de Bages-Sigean-Port-la-Nouvelle. (PhDThesis). University Paul Sabatier, Toulouse, p. 227.

Certain, R., 2002. Morphodynamique d'une côte sableuse microtidale à barres: le Golfe duLion (Languedoc-Roussillon). University of Perpignan, p. 209.

Certain, R., Barusseau, J.-P., 2005. Conceptual modelling of sand bars morphodynamics fora microtidal beach (Sete, France). Bull. Soc. Geol. Fr. 176 (4), 343–354.

Certain, R., Meulé, S., Rey, V., Pinazo, C., 2005a. Wave transformation on a microtidalbarred beach (Sète, France). J. Mar. Syst. 58 (1–2), 19–34.

Certain, R., Tessier, B., Barusseau, J.P., Courp, T., Pauc, H., 2005b. Sedimentary balance andsand stock availability along a littoral system. The case of the western Gulf of Lionslittoral prism (France) investigated by very high resolution seismic. Mar. Pet. Geol.22 (6–7), 889–900.

Cooper, J.A.G., Pilkey, O.H., 2004. Longshore drift: trapped in an expected universe.J. Sediment. Res. 74 (5), 599–606.

Costas, S., Alejo, I., Vila-Concejo, A., Nombela, M.A., 2005. Persistence of storm-inducedmorphology on a modal low-energy beach: a case study from NW-Iberian Peninsula.Mar. Geol. 224 (1–4), 43–56.

Cowell, P.J., Thom, B.G., 1994. Morphodynamics of coastal evolution. In: Carter, R.W.G.,Woodroffe, C.D. (Eds.), Coastal Evolution: Late Quaternary ShorelineMorphodynamics.Cambridge Unviversity Press, Cambridge, pp. 33–86.

191N. Aleman et al. / Geomorphology 241 (2015) 175–191

Davidson, M.A., Splinter, K.D., Turner, I.L., 2013. A simple equilibriummodel for predictingshoreline change. Coast. Eng. 73, 191–202.

Dean, R.G., 1973. Heuristic Model of Sand Transport in the Surf Zone. Coastal Engineering,Sydney.

Durand, P., 1999. L'évolution des plages de l'ouest du Golfe du Lion au XXème Siècle:cinématique du traît de côte, dynamique sédimentaire, analyse prévisionnelle. (PhDThesis). University Lumière of Lyon 2, p. 139.

Ferrer, P., Certain, R., Adloff, F., Bouchette, F., Barusseau, J.-P., Meulé, S., Robin, N., 2011.Hydrodynamics over a microtidal double crescentic barred beach in low energeticconditions (Leucate Beach, France). J. Coast. Res. (SI64), 2032–2036.

Ferrer, P., Certain, R., Barusseau, J.P., Gervais, M., 2009. Conceptual Modelling of a DoubleCrescentic Barred Coast (Leucate Beach, France). Coastal Dynamics, Tokyo, Japan,pp. 1–13.

Folk, R.L., Ward, W.C., 1957. Brazos River bar: a study in the significance of grain size pa-rameters. J. Sediment. Petrol. 27 (1), 3–26.

Gervais, M., 2012. Impacts morphologiques des surcotes et vagues de tempêtes sur le lit-toral méditerranéen. (PhD Thesis). University of Perpignan, p. 399.

Gervais, M., Balouin, Y., Belon, R., 2012. Morphological response and coastal dynamics as-sociated with major storm events along the Gulf of Lions Coastline, France. Geomor-phology 143–144, 69–80.

Gervais, M., Balouin, Y., Thiébot, J., Certain, R., Bélon, R., Pedreros, R., Robin, N., Berne, S.,2011. Morphodynamic evolution of nearshore bars in response to winter storms(Lido de Sète, NW Mediterranean). J. Coast. Res. (SI64), 1855–1860.

Gibbs, R.J., Matthews,M.D., Link, D.A., 1971. The relationship between sphere size and set-tling velocity. J. Sediment. Petrol. 41 (1), 7–18.

Gómez-Pujol, L., Orfila, A., Cañellas, B., Alvarez-Ellacuria, A., Méndez, F.J., Medina, R.,Tintoré, J., 2007. Morphodynamic classification of sandy beaches in low energeticma-rine environment. Mar. Geol. 242 (4), 235–246.

Grasso, F., Michallet, H., Barthélemy, E., Certain, R., 2009. Physical modeling of intermedi-ate cross-shore beachmorphology: transients and equilibrium states. J. Geophys. Res.114 (C9001), 15.

Guizien, K., 2009. Spatial variability of wave conditions in the Gulf of Lions (NW-Mediter-ranean Sea). Life Environ. 59 (3-4), 261–270.

Guza, R.T., Inman, D.L., 1975. Edge waves and beach cups. J. Geophys. Res. 80 (21),2997–3012.

Hegge, B., Elliot, I., Hsu, J., 1996. Sheltered sandy beaches ofsouthwestern Australia.J. Coast. Res. 12 (3), 748–760.

Jackson, D.W.T., Cooper, J.A.G., 2009. Geological control on beach form: accommodationspace and contemporary dynamics. J. Coast. Res. (SI56), 69–72.

Jackson, D.W.T., Cooper, J.A.G., del Rio, L., 2005. Geological control of beachmorphodynamic state. Mar. Geol. 216 (4), 297–314.

Jackson, N.L., Nordstrom, K.F., Eliot, I., Masselink, G., 2002. “Low energy” sandy beaches inmarine and estuarine environments: a review. Geomorphology 48, 147–162.

Jiménez, J.A., Guillén, J., Falqués, A., 2008. Comment on the article “Morphodynamic clas-sification of sandy beaches in low energetic marine environment” by Gómez-Pujol, L.,Orfila, A., Cañellas, B., Alvarez-Ellacuria, A., Méndez, F.J., Medina, R. and Tintoré, J. Ma-rine Geology, 242, pp. 235–246, 2007. Mar. Geol. 255 (1–2), 96–101.

Klein, A.H.F., Ferreira, Ó., Dias, J.M.A., Tessler, M.G., Silveira, L.F., Benedet, L., Menezes, J.T.,De Abreu, J.G.N., 2010. Morphodynamics of structurally controlled headland-baybeaches in southeastern Brazil: a review. Coast. Eng. 57, 98–111.

Klein, A.H.F., Menezes, J.T., 2001. Beachmorphodynamics and profile sequence for a head-land bay coast. J. Coast. Res. 17 (4), 812–835.

L.C.H.F., 1984. La côte méditerranéenne, de la frontière espagnole à la frontière italienne.Catalogue sédimentologique des côtes de France, Parisp. 426.

Levoy, F., Anthony, E., Barusseau, J.-P., Howa, H., Tessier, B., 1998. Morphodynamiqued'une plage macrotidale à barres. Comptes Rendus de l'Académie des Sciences — se-ries IIA. Earth Planet. Sci. 327 (12), 811–818.

Lippmann, T.C., Holman, R.A., 1990. The spatial and temporal variability of sand bar mor-phology. J. Geophys. Res. 92 (C7), 11575–11590.

Loureiro, C., Ferreira, Ó., Cooper, J.A.G., 2013. Applicability of parametric beachmorphodynamic state classification on embayed beaches. Mar. Geol. 346, 153–164.

Masselink, G., Short, A.D., 1993. The effect of tide range on beach morphodynamics andmorphology: a conceptual beach model. J. Coast. Res. 9 (3), 785–800.

Mayençon, R., 1992. In: Edilarge, S.A. (Ed.), Météorologie marine. Imp. Maury.Michel, C., Aleman, N., Robin, N., Certain, R., Guerinel, B., Vanroye, C., Barusseau, J.P.,

Bouchette, F., 2011. Modification des configurations de barres d'avant-côte auvoisinage des ports de plaisance: exemple du Languedoc-Roussillon. 13ème CongrèsFrançais de Sédimentologie. ASF, Dijon, p. 342.

Montreuil, A.-L., Levoy, F., Bretel, P., Anthony, E.J., 2014. Morphological diversity and com-plex sediment recirculation on the ebb delta of a macrotidal inlet (Normandy,France): a multiple LiDAR dataset approach. Geomorphology 219, 114–125.

Muñoz-Perez, J.J., Medina, R., 2010. Comparison of long-, medium- and short-term varia-tions of beach profiles with and without submerged geological control. Coast. Eng. 57(3), 241–251.

Person, R., 1973. Contribution à l'étude thermique du Golfe de Lion. (PhD Thesis). Univer-sity of Paris VI, p. 115.

Price, T.D., Ruessink, B.G., 2011. State dynamics of a double sandbar system. Cont. ShelfRes. 31 (6), 659–674.

Ranasinghe, R., Symonds, G., Black, K., Holman, R., 2004. Morphodynamics of intermediatebeaches: a video imaging and numerical modelling study. Coast. Eng. 51 (7), 629–655.

Robin, N., Certain, R., Bouchette, F., Anthony, E.J., Meulé, S., Aleman, N., 2014. Wave-drivencirculation over a double nearshore bar system during storm conditions. J. Coast. Res.(SI70), 84–89.

Sabatier, F., Stive, M.J.F., Pons, F., 2004. Longshore variation of depth of closure on amicro-tidal wave-dominated coast. International Conference of Coastal Engineering,Lisboapp. 2327–2339.

Sanderson, P.G., Eliot, I., 1999. Compartmentalisation of beachface sediments along thesouthwestern coast of Australia. Mar. Geol. 162, 145–164.

Saye, S.E., van der Wal, D., Pye, K., Blott, S.J., 2005. Beach–dune morphological relation-ships and erosion/accretion: an investigation at five sites in England and Walesusing LIDAR data. Geomorphology 72 (1–4), 128–155.

Scott, T., Masselink, G., Russell, P., 2011. Morphodynamic characteristics and classificationof beaches in England and Wales. Mar. Geol. 286 (1–4), 1–20.

Scott, T., Russell, P., Masselink, G., Wooler, A., Short, A.D., 2007. Beach rescue statistics andtheir relation to nearshore morphology and hazards: a case study for Southwest En-gland. J. Coast. Res. (SI50), 1–6.

Sénéchal, N., Gouriou, T., Castelle, B., Parisot, J.P., Capo, S., Bujan, S., Howa, H., 2009.Morphodynamic response of a meso- to macro-tidal intermediate beach based on along-term data set. Geomorphology 107 (3–4), 263–274.

Short, A.D., 1991. Macro-meso tidal beach morphodynamics — an overview. J. Coast. Res.7 (2), 417–436.

Short, A.D., 1992. Beach systems of the central Netherlands coast: processes, morphologyand structural impacts in a storm driven multi-bar system. Mar. Geol. 107 (1–2),103–132.

Short, A.D., 1999. Handbook of Beach and Shoreface Morphodynamics. Wiley.Short, A.D., 2006. Australian beach systems— nature and distribution. J. Coast. Res. 22 (1),

11–27.Short, A.D., 2010. Role of geological inheritance in Australian beach morphodynamics.

Coast. Eng. 57 (2), 92–97.Short, A.D., Aagaard, T., 1993. Single and multi-bar beach change models. J. Coast. Res.

(SI15), 141–157.Short, A.D., Jackson, D.W.T., 2013. Beach morphodynamics. In: Shroder, J.F. (Ed.), Treatise

on Geomorphology. Elsevier, Academic Press, Amsterdam, San Diego, pp. 106–129.Short, A.D., Woodroffe, C.D., 2009. The Coast of Australia. Cambridge University Press,

Melbourne.Shrestha, R.L., Carter, W.E., Sartori, M., Luzum, B.J., Slatton, K.C., 2005. Airborne Laser

Swath Mapping: quantifying changes in sandy beaches over time scales of weeks toyears. ISPRS J. Photogramm. Remote Sens. 59 (4), 222–232.

Splinter, K.D., Turner, I.L., Davidson, M.A., Barnard, P., Castelle, B., Oltman-Shay, J., 2014. Ageneralized equilibriummodel for predicting daily to interannual shoreline response.J. Geophys. Res. Earth Surf. 119 (9), 1936–1958.

Sunamura, T., Horikawa, K., 1974. Two-dimensional beach transformation due to waves.In: ASCE (Ed.), 14th International Conference in, Coastal Engineering, pp. 920–938.

Sunamura, T., Takeda, I., 1984. Landward migration of inner bars. Mar. Geol. 60 (1–4),63–78.

Thieler, E.R., Pilkey, O.H., Cleary, W.J., Schwab,W.C., 2001. Sedimentation on the shorefaceand inner continental shelf at Wrightsville beach, North Carolina, U.S.A. J. Sediment.Res. 71 (6), 958–970.

Vanroye, C., 2009. Evolution des pratiques de suivi topo-bathymetrique du littoral enLanguedoc-Roussillon: l'utilisation du lidar, Conférence Méditerranéenne Côtière etMaritime. Hammamet, Tunisie, pp. 311–314.

Vousdoukas, M., Velegrakis, A., Karambas, T., Valais, G., Zarkogiannis, S., 2005.Morphodynamics of Beachrock Infected Beaches: Vatera Beach, Northeastern Medi-terranean, Coastal Dynamics. pp. 1–14.

Wright, L.D., Kim, C.S., Hardaway, C.S., Kimball, S.M., Green, M.O., 1987. Shoreline andBeach Dynamics of the Coastal Region From Cape Henry to False Cape, Technical Re-port. Virginia Institut of Marine Science, p. 116.

Wright, L.D., Short, A.D., 1984. Morphodynamic variability of surf zones and beaches: asynthesis. Mar. Geol. 56 (1–4), 93–118.

Wright, L.D., Short, A.D., Green, M.O., 1985. Short-term changes in the morphodynamicstates of beaches and surf zones: an empirical predictive model. Mar. Geol. 62 (3–4), 339–364.

Wright, L.D., Thom, B.G., 1977. Coastal depositional landforms: a morphodynamic ap-proach. Prog. Phys. Geogr. 1 (3), 412–459.